Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer
Abstract The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cance...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Book |
| Published: |
Wiley,
2020-10-01T00:00:00Z.
|
| Subjects: | |
| Online Access: | Connect to this object online. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|